Real-time Adaptive Control Research Articles

Sensing equivalent kinematics enables robot-assisted mirror rehabilitation training via a broaden learning system.

Robot-assisted mirror therapy has been widely developed to help remodeling of premotor cortex for patients suffering from motor disability of limbs. Nevertheless, it is difficult to achieve real-time adaptive control in robot-assisted mirror rehabilitation training, particularly for patients with varying levels of limb impairment. This paper proposes an equivalent kinematics control framework based on the Broaden Learning System model for active robotic mirror rehabilitation, where people's bilateral upper limbs actively perform mirror movements to enhance the impaired limb's participation. The framework accommodates a broaden learning model from sensing multi-kinematic features to adjust the robotic damping coefficient in assisting human participants to complete mirror-symmetry training. Besides, in order to adapt to inter-patients' variability with different disability levels, a challenge-level modification interface is also fused for safer training. This model is verified by additional symmetry indicator such as position trajectory error and force. Experimental results show that the weaker subjects can also maintain mirror movement with the stronger subjects under the help of this model and verify the performance of framework in mirror-symmetry effects and movement smoothness. This leads us to believe that the framework can safely and efficiently assist human participants in completing mirror-symmetry movement. The framework has the potential to improve outcomes in smoother and safer mirror-symmetry training by sensing multi-kinematic features. Future studies are necessary to involve clinical trials with actual patients.

Optimization of sewage treatment processes: Process control based on artificial intelligence

Abstract. The optimization of sewage treatment processes is critical for improving efficiency and reducing energy consumption. This paper explores the application of machine learning and artificial intelligence algorithms in optimizing key processes such as aeration, sedimentation, and filtration. By leveraging real-time monitoring and adaptive control, these algorithms can dynamically adjust operational parameters to enhance treatment efficiency and minimize energy usage. This study provides detailed insights into the implementation and benefits of AI-driven process control in sewage treatment, supported by case studies and data analysis. The findings indicate significant improvements in treatment performance, showcasing the transformative potential of AI in environmental engineering.

Active micro-vibration isolation system for adaptive vibration suppression tests using piezoelectric stack actuator

An active micro-vibration isolation system for adaptive vibration suppression using piezoelectric stack actuators is presented, along with the discrete time modelling method, real-time adaptive controller design, and hardware implementation. The system comprises piezoelectric stack actuators, capacitive displacement sensors, power amplifiers, signal conditioners, and a real-time computer. A discrete asymmetric Bouc-Wen model is developed to characterize the nonlinear behavior of the piezoelectric stack actuators. System identification is conducted using an improved particle swarm optimization method, specifically the Hybrid PSO-Jaya algorithm, which sequentially integrates the PSO algorithm with the Jaya algorithm. Additionally, an improved adaptive filtering algorithm employing affine projection, referred to as the FXAPA algorithm, is introduced to facilitate real-time control. The efficacy of the proposed active micro-vibration isolation technology is validated through micro-vibration suppression tests.

Real-Time Adaptive Inline Acidification Enhances Continuous pH Control for Viral Inactivation.

Existing low pH viral inactivation methods for continuous downstream processing of biologics typically rely on predictive models to estimate the necessary pH adjustments. However, these methods are of limited use during the process development stage due to the dynamic nature of capture chromatography, where batch variations can alter the eluted protein titer. This study introduces an inline viral inactivation system (IVIS) that utilizes real-time adaptive control and inline sensor readings to precisely regulate the pH manipulation for inline acidification and continuous viral inactivation. The IVIS, which includes a coiled flow inversion reactor (CFIR), is integrated with a multicolumn capture chromatography system to demonstrate a fully continuous process from protein capture chromatography to inline pH manipulation. The system achieved precise inline pH manipulation within ±0.15 and a narrow residence time distribution of 13.5min with a relative width of 0.7. The introduction of real-time inline pH manipulation with the IVIS signifies a notable advancement in managing critical process parameters (CPPs) and ensuring consistent product quality across varied production environments for continuous downstream bioprocessing.

A coordinated predictive scheduling and real-time adaptive control for integrated building energy systems with hybrid storage and rooftop PV

A coordinated predictive scheduling and real-time adaptive control for integrated building energy systems with hybrid storage and rooftop PV

Traffic signal phase control at urban isolated intersections: an adaptive strategy utilizing the improved D3QN algorithm

Abstract The intelligent control of traffic signals at urban single intersections has emerged as an effective approach to mitigating urban traffic congestion. However, the existing fixed phase control strategy of traffic signal lights lacks capability to dynamically adjust signal phase switching based on real-time traffic conditions leading to traffic congestion. In this paper, an adaptive real-time control method employed by the traffic signal phase at a single intersection is considered based on the improved Double Dueling Deep Q Network (D3QN) algorithm. Firstly, the traffic signal phase control problem is modeled as a Markov decision process, with its state, action, and reward defined. Subsequently, to enhance the convergence speed and learning performance of the D3QN algorithm, attenuation action selection strategy and priority experience playback technology based on tree summation structure are introduced. Then, traffic flow data from various traffic scenarios are utilized to train the traffic signal control model based on the improved D3QN to obtain the optimal signal phase switch strategy. Finally, the effectiveness and optimal performance of the improved D3QN-based traffic signal control strategy are validated across diverse traffic scenarios. The simulation results show that, compared with the control strategy based on AC, DQN, DDQN, D3QN, and C-D3QN algorithms, the cumulative reward of the proposed I-D3QN strategy is increased by at least 6.57%, and the average queue length and average waiting time are reduced by at least 9.64% and 7.61%, which can effectively reduce the congestion at isolated intersections and significantly improve traffic efficiency.

Real-Time Monitoring and Adaptive Control of Solar Panel Cooling for Enhanced Power Harvest Through IoT Integration

Real-Time Monitoring and Adaptive Control of Solar Panel Cooling for Enhanced Power Harvest Through IoT Integration

Development and Application of Digital Twin Control in Flexible Manufacturing Systems

Flexible manufacturing systems (FMS) are highly adaptable production systems capable of producing a wide range of products in varying quantities. While this flexibility caters to evolving market demands, it also introduces complex scheduling and control challenges, making it difficult to optimize productivity, quality, and energy efficiency. This paper explores the application of digital twin technology to tackle these challenges and enhance FMS optimization and control. A digital twin, constructed by integrating simulation models, data acquisition, and machine learning algorithms, was employed to replicate the behavior of a real-world FMS. This digital twin enabled real-time dynamic optimization and adaptive control of manufacturing operations, facilitating informed decision making and proactive adjustments to optimize resource utilization and process efficiency. Computational experiments were conducted to evaluate the digital twin implementation on an FMS equipped with robotic material handling, CNC machines, and automated inspection. Results demonstrated that the digital twin significantly improved FMS performance. Productivity was enhanced by 14.53% compared to conventional methods, energy consumption was reduced by 13.9%, and quality was increased by 15.8% through intelligent machine coordination. The dynamic optimization and closed-loop control capabilities of the digital twin significantly improved overall equipment effectiveness. This research highlights the transformative potential of digital twins in smart manufacturing systems, paving the way for enhanced productivity, energy efficiency, and defect reduction. The digital twin paradigm offers valuable capabilities in modeling, prediction, optimization, and control, laying the foundation for next-generation FMS.

Active-passive hybrid feed rate control systems in CNC machining: Mitigating force fluctuations and enhancing tool life

Real-time optimization of machining processes for aerospace structural components is imperative due to the difficult-to-cut materials and complex structures. Effective feed rate control in CNC machining plays a key role in achieving high-quality results. While current research trends in mass production emphasize the utilization of adaptive control algorithms and controllers within machining systems, there remains a need to enhance the adaptability of these control systems. This study introduces an active-passive hybrid feed rate control system designed to maintain consistently stable cutting conditions and extend tool life. The hybrid feed rate control system combines offline active pre-compensating, a scheduled pre-compensating feed rate profile, and an online feed rate passive fine-tuning with a real-time adaptive control loop in computer numerical control (CNC) machining. The response speed is enhanced by offline active pre-compensation, whereas the control precision is improved by online passive fine-tuning with a fuzzy controller. Four control cases were tested separately throughout the tool lifespan, including the conventional and adaptive control methods. The proposed adaptive control method reduced the maximum slope from 3.6 to 1.2, demonstrating superior performance compared to both its individual components and other case studies. The results showed a significant 25 % increase in tool life, with a slight decrease in machining efficiency of 7.35 % during the entire tool lifespan.

A digital twin-based traffic light management system using BIRCH algorithm

Urban transportation networks are vital for the economic and environmental well-being of cities and they are faced with the integration of Human-Driven Vehicles (HVs) and Connected and Autonomous Vehicles (CAVs) challenge. Most of the traditional traffic management systems fail to effectively manage the dynamic and complex flows of mixed traffic, mainly because of large computational requirements and the restrictions that control models of traffic lights directly based on extensive and continuous training data. Most of the times, the operational flexibility of CAVs is severely compromised for the safety of HVs, or CAVs are given high priority without taking into account the efficiency of HVs leading to lower performance, especially at low CAV penetration rates. On the other hand, the existing adaptive traffic light approaches were usually partial and could not adapt to the real-time behaviors of the traffic system. Some systems operate with inflexible temporal control plans that cannot react to variations in traffic flow or use adaptive control strategies that are based on a limited set of static traffic conditions. This paper presents a novel traffic light control approach utilizing the BIRCH (Balanced Iterative Reducing and Clustering using Hierarchies) clustering algorithm combined with digital twins for a more adaptive and efficient system. The BIRCH is effective in processing large datasets because it clusters data points incrementally and dynamically into a small set of representatives. The suggested method does not only enable better simulation and prediction of traffic patterns but also makes possible the real-time adaptive control of traffic signals at signalized intersections. It also improves traffic flow, reduces congestion, and minimizes vehicle idling time by adjusting the green and red light durations dynamically based on both real-time and historical traffic data. This approach is assessed under different traffic intensities, which include low, moderate, and high, while efficiency, fuel consumption, and the number of stops are being compared with the traditional and the existing adaptive traffic management systems.

Enhancing hybrid manufacturing with AI-driven real-time adaptive process control: integrating machine learning models and robotic systems

Enhancing hybrid manufacturing with AI-driven real-time adaptive process control: integrating machine learning models and robotic systems

Optimization of GMAW Processes in Robotic Welding: A Case Study from Automotive Manufacturing

The optimization of Gas Metal Arc Welding (GMAW) processes in robotic welding has become crucial for enhancing efficiency and quality in automotive manufacturing. This paper explores the integration of advanced robotic welding technologies, focusing on the use of the KUKA KR8 L8 Arc HW robot in GMAW applications. By leveraging artificial intelligence and sensor integration, this study demonstrates significant improvements in welding precision and process stability. Key advancements include the use of geometric and process sensors for real-time adaptive control, and the implementation of Fronius CMT for superior arc control and penetration. Detailed case studies from automotive manufacturing illustrate the practical benefits of these technologies, showcasing enhanced productivity, reduced cycle times, and improved weld quality. The findings underscore the importance of continuous innovation and optimization in robotic welding processes to meet the evolving demands of the automotive industry.

Development of an Intelligent Road-Crossing Sensor System for Enhanced Pedestrian Safety

Pedestrian safety at road crossings in urban environments is a critical issue due to the increasing vehicular traffic and high pedestrian density. Traditional traffic control measures often fail to adequately address the dynamic interactions between vehicles and pedestrians, leading to significant risks of accidents. This study proposes the development of an intelligent road-crossing sensor system leveraging advanced technologies such as sensors, artificial intelligence (AI), and the Internet of Things (IoT). The system aims to enhance pedestrian safety by providing real-time alerts and adaptive traffic control, thereby reducing the risk of pedestrian-vehicle collisions. The methodology includes sensor selection, data collection, machine learning model development, and system implementation and testing. The potential benefits and challenges of deploying such systems are also discussed, offering insights into creating smarter and safer urban environments. Keywords: Pedestrian safety, intelligent sensor systems, road crossing, vehicle detection, traffic signals

Leveraging Machine Learning to Improve Access Control Mechanisms in Data Warehousing

This paper delves into integrating Machine Learning (ML) algorithms into Access Control Mechanisms (ACM) within Data Warehouses (DW) to enhance both security and operational efficiency. Traditional ACMs, notably role-based access control (RBAC), often struggle with adapting to dynamic threats and complex access patterns. ML tackles these issues by improving access decision accuracy through real-time anomaly detection and adaptive control based on user behavior and contextual insights. The study examines the implementation and benefits of ML-enhanced ACMs in DW environments, highlighting their effectiveness in mitigating unauthorized access and insider threats. This research also addresses critical considerations, such as privacy protections and the interpretability of ML models, crucial for maintaining regulatory compliance and stakeholder trust. Ultimately, this study emphasizes ML's pivotal contribution to advancing security practices within Data Warehouses (DWs) and proposes future research directions to enhance adaptive security measures in evolving digital landscapes.

Adaptive real-time ECMS with equivalent factor optimization for plug-in hybrid electric buses

—The plug-in hybrid electric bus (PHEB) is one of the vehicles that can address global environmental and energy problems. Due to the complex characteristics of the urban cycle condition, a fixed parameter energy management strategy may not be suitable for fuel economy. Thus, an adaptive real-time control strategy seems to be of great significance for PHEBs. The paper designs an advanced equivalent consumption minimization strategy (ECMS) with segments divided from the driving cycle and optimized equivalent factor (EF). The classification of different segments is based on the time that the vehicle stops. The EF is optimized under two different driving conditions by using the grey wolf optimization (GWO) algorithm. A new model, as the difference between the actual state of charge (SOC) and the reference SOC, is presented and minimized by optimizing the EF. Segmented EF optimization adds a correction factor, which can adjust EF according to the kinematic characteristics of each segment. The results show that the fuel consumption decreased by 18.72 % compared with the conventional ECMS.

ILP-based resource optimization realized by quantum annealing for optical wide-area communication networks—A framework for solving combinatorial problems of a real-world application by quantum annealing

Resource allocation of wide-area internet networks is inherently a combinatorial optimization problem that if solved quickly, could provide near real-time adaptive control of internet-protocol traffic ensuring increased network efficacy and robustness, while minimizing energy requirements coming from power-hungry transceivers. In recent works we demonstrated how such a problem could be cast as a quadratic unconstrained binary optimization (QUBO) problem that can be embedded onto the D-Wave Advantage™ quantum annealer system, demonstrating proof of principle. Our initial studies left open the possibility for improvement of D-Wave solutions via judicious choices of system run parameters. Here we report on our investigations for optimizing these system parameters, and how we incorporate machine learning (ML) techniques to further improve on the quality of solutions. In particular, we use the Hamming distance to investigate correlations between various system-run parameters and solution vectors. We then apply a decision tree neural network (NN) to learn these correlations, with the goal of using the neural network to provide further guesses to solution vectors. We successfully implement this NN in a simple integer linear programming (ILP) example, demonstrating how the NN can fully map out the solution space that was not captured by D-Wave. We find, however, for the 3-node network problem the NN is not able to enhance the quality of space of solutions.

Decision Tree Variations and Online Tuning for Real-Time Control of a Building in a Two-Stage Management Strategy

This study examines the use of data-driven controllers for near real-time control of an HVAC and storage system in a residential building. The work is based on a two-stage management with, first, a day-ahead optimal scheduling, and second, a near real-time adaptive control to remain close to the commitments made in the first stage. A Model Predictive Control (MPC) is adopted from previous works from the authors. The aim of this paper is then to explore lightweight controllers for the real-time stage as alternatives to MPC, which relies on computational-intensive modeling and optimization. Decision Trees (DTs) are considered for this purpose, offering understandable solutions by processing input data through explicit tests of the inputs with predefined thresholds. Various DT variations, including regular, regressors, and linear DTs, are studied. Linear DTs, with a minimal number of leaves, exhibit superior performance, especially when trained on historical MPC data, outperforming the reference MPC in terms of energy exchange efficiency. However, due to impracticalities, an offline training approach for the DTs is proposed, which sacrifices performance. An online tuning strategy is then introduced, updating the DT coefficients based on real-time observations, significantly enhancing performance in terms of energy deviation reduction during real-time operation.

Highest fusion performance without harmful edge energy bursts in tokamak

The path of tokamak fusion and International thermonuclear experimental reactor (ITER) is maintaining high-performance plasma to produce sufficient fusion power. This effort is hindered by the transient energy burst arising from the instabilities at the boundary of plasmas. Conventional 3D magnetic perturbations used to suppress these instabilities often degrade fusion performance and increase the risk of other instabilities. This study presents an innovative 3D field optimization approach that leverages machine learning and real-time adaptability to overcome these challenges. Implemented in the DIII-D and KSTAR tokamaks, this method has consistently achieved reactor-relevant core confinement and the highest fusion performance without triggering damaging bursts. This is enabled by advances in the physics understanding of self-organized transport in the plasma edge and machine learning techniques to optimize the 3D field spectrum. The success of automated, real-time adaptive control of such complex systems paves the way for maximizing fusion efficiency in ITER and beyond while minimizing damage to device components.

Designing a Real-Time Implementable Optimal Adaptive Cruise Control for Improving Battery Health and Energy Consumption in EVs through V2V Communication

Battery electric vehicles (BEVs) face challenges like their limited all-electric range, the discrepancy between promised and actual energy efficiency, and battery health degradation, despite their environmental benefits. This article proposes an optimal adaptive cruise control (OACC) framework by leveraging ideal vehicle-to-vehicle communication to address these challenges. In a connected vehicle environment, where it is assumed that the Ego vehicle’s vehicle control unit (VCU) accurately knows the speed and position of the Leading vehicle, the VCU can optimally plan the acceleration trajectory for a short-term future time window through a model predictive control (MPC) framework tailored to BEVs. The primary objective of the OACC is to reduce the energy consumption and battery state-of-health degradation of a BEV. The Chevrolet Spark 2015 is chosen as the BEV platform used to validate the effectiveness of the proposed OACC. Simulations conducted under urban and highway driving conditions, as well as under communication delay and infused noise, resulted in up to a 3.7% reduction in energy consumption and a 9.7% reduction in battery state-of-health (SOH) degradation, demonstrating the effectiveness and robustness of the proposed OACC.

IoT and Machine Learning based Precision Agriculture through the Integration of Wireless Sensor Networks

In this study, the integration of IoT technology and machine learning (ML) algorithms with precision agriculture are studied, with an emphasis on optimizing irrigation management through wireless sensor networks. Data are collected in real time from various sensors that measure temperature, humidity, moisture in the soil and water levels. Such data is used to predict the demands for water and control pump operations based on them. They employed different machine learning (ML) models, including Support Vector Machine (SVM), Decision Trees (DT), Random Forest (RF) and Naive Bayes (NB). The accuracy of pump operation predictions is then evaluated. SVM presents the highest overall accuracy. Followed closely by DT and RF. Confusion matrices can be used to glean insights into the misclassification patterns of each model, guiding the selection of the most effective ML approach for precision agriculture applications. IoT and ML technologies together enable real-time monitoring, adaptive control, and data-driven decision-making in agriculture, all of which can help towards efficiently using water resources in dealing with climate change and rapidly growing food demands.